Overview

Dataset statistics

Number of variables34
Number of observations81412
Missing cells11149
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.0 MiB
Average record size in memory258.0 B

Variable types

Numeric14
Categorical14
Boolean6

Alerts

medical_specialty has a high cardinality: 71 distinct values High cardinality
diag_1 has a high cardinality: 701 distinct values High cardinality
diag_2 has a high cardinality: 725 distinct values High cardinality
diag_3 has a high cardinality: 761 distinct values High cardinality
change is highly correlated with diabetesMed and 1 other fieldsHigh correlation
diabetesMed is highly correlated with change and 1 other fieldsHigh correlation
insulin is highly correlated with change and 1 other fieldsHigh correlation
gender is highly correlated with hemoglobin_levelHigh correlation
age is highly correlated with medical_specialtyHigh correlation
admission_type_code is highly correlated with admission_source_code and 1 other fieldsHigh correlation
admission_source_code is highly correlated with admission_type_codeHigh correlation
medical_specialty is highly correlated with age and 1 other fieldsHigh correlation
hemoglobin_level is highly correlated with genderHigh correlation
insulin is highly correlated with change and 1 other fieldsHigh correlation
change is highly correlated with insulin and 1 other fieldsHigh correlation
diabetesMed is highly correlated with insulin and 1 other fieldsHigh correlation
age has 2336 (2.9%) missing values Missing
weight has 1560 (1.9%) missing values Missing
admission_type_code has 1162 (1.4%) missing values Missing
num_lab_procedures has 1493 (1.8%) missing values Missing
num_medications has 2678 (3.3%) missing values Missing
diag_2 has 1349 (1.7%) missing values Missing
admission_id is uniformly distributed Uniform
admission_id has unique values Unique
num_procedures has 37355 (45.9%) zeros Zeros
number_outpatient has 67984 (83.5%) zeros Zeros
number_emergency has 72350 (88.9%) zeros Zeros
number_inpatient has 53995 (66.3%) zeros Zeros

Reproduction

Analysis started2022-02-01 17:22:29.832680
Analysis finished2022-02-01 17:23:27.469446
Duration57.64 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

admission_id
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct81412
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40705.5
Minimum0
Maximum81411
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size636.2 KiB
2022-02-01T17:23:27.705248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4070.55
Q120352.75
median40705.5
Q361058.25
95-th percentile77340.45
Maximum81411
Range81411
Interquartile range (IQR)40705.5

Descriptive statistics

Standard deviation23501.76439
Coefficient of variation (CV)0.5773609069
Kurtosis-1.2
Mean40705.5
Median Absolute Deviation (MAD)20353
Skewness0
Sum3313916166
Variance552332929.7
MonotonicityStrictly increasing
2022-02-01T17:23:27.933915image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
791801
 
< 0.1%
198111
 
< 0.1%
177621
 
< 0.1%
239051
 
< 0.1%
218561
 
< 0.1%
771351
 
< 0.1%
750861
 
< 0.1%
812291
 
< 0.1%
689391
 
< 0.1%
Other values (81402)81402
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
814111
< 0.1%
814101
< 0.1%
814091
< 0.1%
814081
< 0.1%
814071
< 0.1%
814061
< 0.1%
814051
< 0.1%
814041
< 0.1%
814031
< 0.1%
814021
< 0.1%

patient_id
Real number (ℝ≥0)

Distinct60069
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108639531.1
Minimum198
Maximum379005166
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.2 KiB
2022-02-01T17:23:28.191794image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum198
5-th percentile2914233.3
Q146839060
median90834372
Q3175111722
95-th percentile222996661.2
Maximum379005166
Range379004968
Interquartile range (IQR)128272662

Descriptive statistics

Standard deviation77324533.16
Coefficient of variation (CV)0.7117531928
Kurtosis-0.3648068986
Mean108639531.1
Median Absolute Deviation (MAD)65849112
Skewness0.4666683655
Sum8.844561509 × 1012
Variance5.979083428 × 1015
MonotonicityNot monotonic
2022-02-01T17:23:28.406918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17757171033
 
< 0.1%
17645500820
 
< 0.1%
18121953619
 
< 0.1%
18541863018
 
< 0.1%
4728673818
 
< 0.1%
332051418
 
< 0.1%
18232048817
 
< 0.1%
4639797017
 
< 0.1%
8628174016
 
< 0.1%
16885715416
 
< 0.1%
Other values (60059)81220
99.8%
ValueCountFrequency (%)
1982
< 0.1%
6841
 
< 0.1%
13861
 
< 0.1%
14761
 
< 0.1%
17821
 
< 0.1%
22324
< 0.1%
25381
 
< 0.1%
25563
< 0.1%
31861
 
< 0.1%
39781
 
< 0.1%
ValueCountFrequency (%)
3790051661
< 0.1%
3787021181
< 0.1%
3786987881
< 0.1%
3786641021
< 0.1%
3785156201
< 0.1%
3784314521
< 0.1%
3783585701
< 0.1%
3782586341
< 0.1%
3781945901
< 0.1%
3781511561
< 0.1%

race
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size636.2 KiB
Caucasian
48733 
AfricanAmerican
9209 
White
6015 
African American
 
3067
WHITE
 
3047
Other values (10)
11341 

Length

Max length16
Median length9
Mean length9.144622414
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCaucasian
2nd rowCaucasian
3rd rowEURO
4th rowBlack
5th rowCaucasian

Common Values

ValueCountFrequency (%)
Caucasian48733
59.9%
AfricanAmerican9209
 
11.3%
White6015
 
7.4%
African American3067
 
3.8%
WHITE3047
 
3.7%
European2491
 
3.1%
?1827
 
2.2%
Black1545
 
1.9%
Afro American1411
 
1.7%
Hispanic1305
 
1.6%
Other values (5)2762
 
3.4%

Length

2022-02-01T17:23:28.613679image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
caucasian48733
56.7%
africanamerican9365
 
10.9%
white9062
 
10.6%
american4478
 
5.2%
african3067
 
3.6%
european2491
 
2.9%
1827
 
2.1%
black1545
 
1.8%
afro1411
 
1.6%
hispanic1305
 
1.5%
Other values (4)2606
 
3.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

gender
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size636.2 KiB
Female
43719 
Male
37691 
Unknown/Invalid
 
2

Length

Max length15
Median length6
Mean length5.074288803
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female43719
53.7%
Male37691
46.3%
Unknown/Invalid2
 
< 0.1%

Length

2022-02-01T17:23:28.793962image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T17:23:28.945369image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
female43719
53.7%
male37691
46.3%
unknown/invalid2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age
Categorical

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)< 0.1%
Missing2336
Missing (%)2.9%
Memory size636.2 KiB
[70-80)
20261 
[60-70)
17414 
[50-60)
13414 
[80-90)
13383 
[40-50)
7498 
Other values (5)
7106 

Length

Max length8
Median length7
Mean length7.025747382
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[50-60)
2nd row[80-90)
3rd row[60-70)
4th row[70-80)
5th row[70-80)

Common Values

ValueCountFrequency (%)
[70-80)20261
24.9%
[60-70)17414
21.4%
[50-60)13414
16.5%
[80-90)13383
16.4%
[40-50)7498
 
9.2%
[30-40)2964
 
3.6%
[90-100)2172
 
2.7%
[20-30)1297
 
1.6%
[10-20)537
 
0.7%
[0-10)136
 
0.2%
(Missing)2336
 
2.9%

Length

2022-02-01T17:23:29.052500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T17:23:29.226836image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
70-8020261
25.6%
60-7017414
22.0%
50-6013414
17.0%
80-9013383
16.9%
40-507498
 
9.5%
30-402964
 
3.7%
90-1002172
 
2.7%
20-301297
 
1.6%
10-20537
 
0.7%
0-10136
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

weight
Categorical

MISSING

Distinct10
Distinct (%)< 0.1%
Missing1560
Missing (%)1.9%
Memory size636.2 KiB
?
77353 
[75-100)
 
1037
[50-75)
 
713
[100-125)
 
482
[125-150)
 
117
Other values (5)
 
150

Length

Max length9
Median length1
Mean length1.216024646
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd row?
3rd row?
4th row?
5th row?

Common Values

ValueCountFrequency (%)
?77353
95.0%
[75-100)1037
 
1.3%
[50-75)713
 
0.9%
[100-125)482
 
0.6%
[125-150)117
 
0.1%
[25-50)72
 
0.1%
[0-25)40
 
< 0.1%
[150-175)27
 
< 0.1%
[175-200)8
 
< 0.1%
>2003
 
< 0.1%
(Missing)1560
 
1.9%

Length

2022-02-01T17:23:29.386341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T17:23:29.545031image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
77353
96.9%
75-1001037
 
1.3%
50-75713
 
0.9%
100-125482
 
0.6%
125-150117
 
0.1%
25-5072
 
0.1%
0-2540
 
0.1%
150-17527
 
< 0.1%
175-2008
 
< 0.1%
2003
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

admission_type_code
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)< 0.1%
Missing1162
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean2.024598131
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.2 KiB
2022-02-01T17:23:29.667982image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.44601519
Coefficient of variation (CV)0.7142233155
Kurtosis1.942273762
Mean2.024598131
Median Absolute Deviation (MAD)0
Skewness1.591920787
Sum162474
Variance2.090959928
MonotonicityNot monotonic
2022-02-01T17:23:29.829281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
142562
52.3%
314884
 
18.3%
214576
 
17.9%
64182
 
5.1%
53768
 
4.6%
8254
 
0.3%
716
 
< 0.1%
48
 
< 0.1%
(Missing)1162
 
1.4%
ValueCountFrequency (%)
142562
52.3%
214576
 
17.9%
314884
 
18.3%
48
 
< 0.1%
53768
 
4.6%
64182
 
5.1%
716
 
< 0.1%
8254
 
0.3%
ValueCountFrequency (%)
8254
 
0.3%
716
 
< 0.1%
64182
 
5.1%
53768
 
4.6%
48
 
< 0.1%
314884
 
18.3%
214576
 
17.9%
142562
52.3%

discharge_disposition_code
Real number (ℝ≥0)

Distinct25
Distinct (%)< 0.1%
Missing571
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean3.711260375
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.2 KiB
2022-02-01T17:23:30.053091image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile18
Maximum28
Range27
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.273755472
Coefficient of variation (CV)1.421014679
Kurtosis6.031098616
Mean3.711260375
Median Absolute Deviation (MAD)0
Skewness2.567316037
Sum300022
Variance27.81249678
MonotonicityNot monotonic
2022-02-01T17:23:30.245187image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
147854
58.8%
311097
 
13.6%
610244
 
12.6%
182912
 
3.6%
21690
 
2.1%
221580
 
1.9%
111312
 
1.6%
5936
 
1.1%
25793
 
1.0%
4650
 
0.8%
Other values (15)1773
 
2.2%
(Missing)571
 
0.7%
ValueCountFrequency (%)
147854
58.8%
21690
 
2.1%
311097
 
13.6%
4650
 
0.8%
5936
 
1.1%
610244
 
12.6%
7506
 
0.6%
885
 
0.1%
918
 
< 0.1%
105
 
< 0.1%
ValueCountFrequency (%)
28106
 
0.1%
274
 
< 0.1%
25793
 
1.0%
2440
 
< 0.1%
23324
 
0.4%
221580
1.9%
195
 
< 0.1%
182912
3.6%
1712
 
< 0.1%
167
 
< 0.1%

admission_source_code
Real number (ℝ≥0)

HIGH CORRELATION

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.750122832
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.2 KiB
2022-02-01T17:23:30.413768image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q37
95-th percentile17
Maximum25
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.063455702
Coefficient of variation (CV)0.7066728521
Kurtosis1.749926677
Mean5.750122832
Median Absolute Deviation (MAD)0
Skewness1.031186893
Sum468129
Variance16.51167224
MonotonicityNot monotonic
2022-02-01T17:23:30.583444image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
745942
56.4%
123684
29.1%
175412
 
6.6%
42581
 
3.2%
61819
 
2.2%
2878
 
1.1%
5681
 
0.8%
3148
 
0.2%
20130
 
0.2%
999
 
0.1%
Other values (7)38
 
< 0.1%
ValueCountFrequency (%)
123684
29.1%
2878
 
1.1%
3148
 
0.2%
42581
 
3.2%
5681
 
0.8%
61819
 
2.2%
745942
56.4%
815
 
< 0.1%
999
 
0.1%
106
 
< 0.1%
ValueCountFrequency (%)
252
 
< 0.1%
2210
 
< 0.1%
20130
 
0.2%
175412
6.6%
142
 
< 0.1%
131
 
< 0.1%
112
 
< 0.1%
106
 
< 0.1%
999
 
0.1%
815
 
< 0.1%

time_in_hospital
Real number (ℝ≥0)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.395924434
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.2 KiB
2022-02-01T17:23:30.748430image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum14
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.975844099
Coefficient of variation (CV)0.6769552444
Kurtosis0.8490950317
Mean4.395924434
Median Absolute Deviation (MAD)2
Skewness1.130728188
Sum357881
Variance8.855648103
MonotonicityNot monotonic
2022-02-01T17:23:30.922221image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
314223
17.5%
213723
16.9%
111302
13.9%
411228
13.8%
57975
9.8%
66059
7.4%
74717
 
5.8%
83528
 
4.3%
92372
 
2.9%
101886
 
2.3%
Other values (4)4399
 
5.4%
ValueCountFrequency (%)
111302
13.9%
213723
16.9%
314223
17.5%
411228
13.8%
57975
9.8%
66059
7.4%
74717
 
5.8%
83528
 
4.3%
92372
 
2.9%
101886
 
2.3%
ValueCountFrequency (%)
14814
 
1.0%
13944
 
1.2%
121153
 
1.4%
111488
 
1.8%
101886
 
2.3%
92372
 
2.9%
83528
4.3%
74717
5.8%
66059
7.4%
57975
9.8%

payer_code
Categorical

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size636.2 KiB
?
32278 
MC
25952 
HM
4973 
SP
4003 
BC
3718 
Other values (13)
10488 

Length

Max length2
Median length2
Mean length1.603522822
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row?
2nd rowMC
3rd rowMD
4th row?
5th rowMC

Common Values

ValueCountFrequency (%)
?32278
39.6%
MC25952
31.9%
HM4973
 
6.1%
SP4003
 
4.9%
BC3718
 
4.6%
MD2824
 
3.5%
CP2053
 
2.5%
UN1925
 
2.4%
CM1551
 
1.9%
OG795
 
1.0%
Other values (8)1340
 
1.6%

Length

2022-02-01T17:23:31.099465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
32278
39.6%
mc25952
31.9%
hm4973
 
6.1%
sp4003
 
4.9%
bc3718
 
4.6%
md2824
 
3.5%
cp2053
 
2.5%
un1925
 
2.4%
cm1551
 
1.9%
og795
 
1.0%
Other values (8)1340
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

medical_specialty
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct71
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size636.2 KiB
?
40020 
InternalMedicine
11712 
Emergency/Trauma
6021 
Family/GeneralPractice
5939 
Cardiology
4273 
Other values (66)
13447 

Length

Max length36
Median length8
Mean length8.599199135
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st row?
2nd rowEmergency/Trauma
3rd row?
4th row?
5th row?

Common Values

ValueCountFrequency (%)
?40020
49.2%
InternalMedicine11712
 
14.4%
Emergency/Trauma6021
 
7.4%
Family/GeneralPractice5939
 
7.3%
Cardiology4273
 
5.2%
Surgery-General2473
 
3.0%
Nephrology1299
 
1.6%
Orthopedics1100
 
1.4%
Orthopedics-Reconstructive981
 
1.2%
Radiologist913
 
1.1%
Other values (61)6681
 
8.2%

Length

2022-02-01T17:23:31.294555image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
40020
49.2%
internalmedicine11712
 
14.4%
emergency/trauma6021
 
7.4%
family/generalpractice5939
 
7.3%
cardiology4273
 
5.2%
surgery-general2473
 
3.0%
nephrology1299
 
1.6%
orthopedics1100
 
1.4%
orthopedics-reconstructive981
 
1.2%
radiologist913
 
1.1%
Other values (61)6681
 
8.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.6 KiB
False
80550 
True
 
862
ValueCountFrequency (%)
False80550
98.9%
True862
 
1.1%
2022-02-01T17:23:31.445889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size636.2 KiB
Complete
67114 
Incomplete
13978 
None
 
320

Length

Max length10
Median length8
Mean length8.327666683
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowComplete
2nd rowComplete
3rd rowComplete
4th rowComplete
5th rowComplete

Common Values

ValueCountFrequency (%)
Complete67114
82.4%
Incomplete13978
 
17.2%
None320
 
0.4%

Length

2022-02-01T17:23:31.534805image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T17:23:31.670231image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
complete67114
82.4%
incomplete13978
 
17.2%
none320
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

num_lab_procedures
Real number (ℝ≥0)

MISSING

Distinct115
Distinct (%)0.1%
Missing1493
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean43.07119709
Minimum1
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.2 KiB
2022-02-01T17:23:31.776282image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q132
median44
Q357
95-th percentile73
Maximum132
Range131
Interquartile range (IQR)25

Descriptive statistics

Standard deviation19.63040493
Coefficient of variation (CV)0.4557664114
Kurtosis-0.2441755168
Mean43.07119709
Median Absolute Deviation (MAD)13
Skewness-0.2404533528
Sum3442207
Variance385.3527977
MonotonicityNot monotonic
2022-02-01T17:23:31.995241image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12533
 
3.1%
432207
 
2.7%
441988
 
2.4%
451884
 
2.3%
381764
 
2.2%
401736
 
2.1%
461720
 
2.1%
411718
 
2.1%
471679
 
2.1%
391662
 
2.0%
Other values (105)61028
75.0%
ValueCountFrequency (%)
12533
3.1%
2858
 
1.1%
3523
 
0.6%
4285
 
0.4%
5231
 
0.3%
6214
 
0.3%
7258
 
0.3%
8281
 
0.3%
9728
 
0.9%
10655
 
0.8%
ValueCountFrequency (%)
1321
 
< 0.1%
1261
 
< 0.1%
1211
 
< 0.1%
1181
 
< 0.1%
1142
< 0.1%
1131
 
< 0.1%
1112
< 0.1%
1093
< 0.1%
1083
< 0.1%
1064
< 0.1%

num_procedures
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.341767798
Minimum0
Maximum6
Zeros37355
Zeros (%)45.9%
Negative0
Negative (%)0.0%
Memory size636.2 KiB
2022-02-01T17:23:32.498771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.708464781
Coefficient of variation (CV)1.273293921
Kurtosis0.8445664498
Mean1.341767798
Median Absolute Deviation (MAD)1
Skewness1.313355194
Sum109236
Variance2.918851909
MonotonicityNot monotonic
2022-02-01T17:23:32.653802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
037355
45.9%
116513
20.3%
210162
 
12.5%
37548
 
9.3%
63994
 
4.9%
43409
 
4.2%
52431
 
3.0%
ValueCountFrequency (%)
037355
45.9%
116513
20.3%
210162
 
12.5%
37548
 
9.3%
43409
 
4.2%
52431
 
3.0%
63994
 
4.9%
ValueCountFrequency (%)
63994
 
4.9%
52431
 
3.0%
43409
 
4.2%
37548
 
9.3%
210162
 
12.5%
116513
20.3%
037355
45.9%

num_medications
Real number (ℝ≥0)

MISSING

Distinct73
Distinct (%)0.1%
Missing2678
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean16.02442401
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.2 KiB
2022-02-01T17:23:32.847703image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median15
Q320
95-th percentile31
Maximum81
Range80
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.107234783
Coefficient of variation (CV)0.5059298717
Kurtosis3.427614554
Mean16.02442401
Median Absolute Deviation (MAD)5
Skewness1.316139858
Sum1261667
Variance65.72725582
MonotonicityNot monotonic
2022-02-01T17:23:33.060318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
134714
 
5.8%
124636
 
5.7%
114457
 
5.5%
154443
 
5.5%
144436
 
5.4%
164234
 
5.2%
104108
 
5.0%
173817
 
4.7%
93813
 
4.7%
183523
 
4.3%
Other values (63)36553
44.9%
ValueCountFrequency (%)
1203
 
0.2%
2357
 
0.4%
3687
 
0.8%
41107
 
1.4%
51570
 
1.9%
62094
2.6%
72662
3.3%
83356
4.1%
93813
4.7%
104108
5.0%
ValueCountFrequency (%)
811
 
< 0.1%
752
 
< 0.1%
741
 
< 0.1%
702
 
< 0.1%
695
< 0.1%
686
< 0.1%
676
< 0.1%
663
 
< 0.1%
658
< 0.1%
647
< 0.1%

number_outpatient
Real number (ℝ≥0)

ZEROS

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3709526851
Minimum0
Maximum42
Zeros67984
Zeros (%)83.5%
Negative0
Negative (%)0.0%
Memory size636.2 KiB
2022-02-01T17:23:33.271737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.278537861
Coefficient of variation (CV)3.446633256
Kurtosis152.4729964
Mean0.3709526851
Median Absolute Deviation (MAD)0
Skewness8.984331589
Sum30200
Variance1.634659062
MonotonicityNot monotonic
2022-02-01T17:23:33.466105image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
067984
83.5%
16862
 
8.4%
22904
 
3.6%
31618
 
2.0%
4873
 
1.1%
5420
 
0.5%
6238
 
0.3%
7129
 
0.2%
876
 
0.1%
973
 
0.1%
Other values (29)235
 
0.3%
ValueCountFrequency (%)
067984
83.5%
16862
 
8.4%
22904
 
3.6%
31618
 
2.0%
4873
 
1.1%
5420
 
0.5%
6238
 
0.3%
7129
 
0.2%
876
 
0.1%
973
 
0.1%
ValueCountFrequency (%)
421
< 0.1%
401
< 0.1%
391
< 0.1%
381
< 0.1%
371
< 0.1%
361
< 0.1%
351
< 0.1%
341
< 0.1%
332
< 0.1%
292
< 0.1%

number_emergency
Real number (ℝ≥0)

ZEROS

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1975875792
Minimum0
Maximum64
Zeros72350
Zeros (%)88.9%
Negative0
Negative (%)0.0%
Memory size636.2 KiB
2022-02-01T17:23:33.654366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum64
Range64
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8812896989
Coefficient of variation (CV)4.460248475
Kurtosis638.5493694
Mean0.1975875792
Median Absolute Deviation (MAD)0
Skewness16.40885167
Sum16086
Variance0.7766715333
MonotonicityNot monotonic
2022-02-01T17:23:33.838587image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
072350
88.9%
16064
 
7.4%
21646
 
2.0%
3588
 
0.7%
4292
 
0.4%
5167
 
0.2%
682
 
0.1%
755
 
0.1%
839
 
< 0.1%
927
 
< 0.1%
Other values (19)102
 
0.1%
ValueCountFrequency (%)
072350
88.9%
16064
 
7.4%
21646
 
2.0%
3588
 
0.7%
4292
 
0.4%
5167
 
0.2%
682
 
0.1%
755
 
0.1%
839
 
< 0.1%
927
 
< 0.1%
ValueCountFrequency (%)
641
 
< 0.1%
461
 
< 0.1%
421
 
< 0.1%
291
 
< 0.1%
281
 
< 0.1%
252
< 0.1%
241
 
< 0.1%
224
< 0.1%
212
< 0.1%
204
< 0.1%

number_inpatient
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6377929544
Minimum0
Maximum21
Zeros53995
Zeros (%)66.3%
Negative0
Negative (%)0.0%
Memory size636.2 KiB
2022-02-01T17:23:34.036822image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.265472414
Coefficient of variation (CV)1.984142981
Kurtosis20.83213029
Mean0.6377929544
Median Absolute Deviation (MAD)0
Skewness3.619760625
Sum51924
Variance1.60142043
MonotonicityNot monotonic
2022-02-01T17:23:34.206347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
053995
66.3%
115706
 
19.3%
26057
 
7.4%
32747
 
3.4%
41288
 
1.6%
5641
 
0.8%
6386
 
0.5%
7224
 
0.3%
8120
 
0.1%
993
 
0.1%
Other values (11)155
 
0.2%
ValueCountFrequency (%)
053995
66.3%
115706
 
19.3%
26057
 
7.4%
32747
 
3.4%
41288
 
1.6%
5641
 
0.8%
6386
 
0.5%
7224
 
0.3%
8120
 
0.1%
993
 
0.1%
ValueCountFrequency (%)
211
 
< 0.1%
192
 
< 0.1%
181
 
< 0.1%
171
 
< 0.1%
164
 
< 0.1%
157
 
< 0.1%
146
 
< 0.1%
1316
 
< 0.1%
1230
< 0.1%
1140
< 0.1%

diag_1
Categorical

HIGH CARDINALITY

Distinct701
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size636.2 KiB
428
 
5429
414
 
5270
786
 
3232
410
 
2914
486
 
2776
Other values (696)
61791 

Length

Max length6
Median length3
Mean length3.17798359
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)0.1%

Sample

1st row721
2nd row276
3rd row414
4th row577
5th row531

Common Values

ValueCountFrequency (%)
4285429
 
6.7%
4145270
 
6.5%
7863232
 
4.0%
4102914
 
3.6%
4862776
 
3.4%
4272218
 
2.7%
4911811
 
2.2%
7151691
 
2.1%
7801620
 
2.0%
6821612
 
2.0%
Other values (691)52839
64.9%

Length

2022-02-01T17:23:34.402143image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4285429
 
6.7%
4145270
 
6.5%
7863232
 
4.0%
4102914
 
3.6%
4862776
 
3.4%
4272218
 
2.7%
4911811
 
2.2%
7151691
 
2.1%
7801620
 
2.0%
6821612
 
2.0%
Other values (691)52839
64.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diag_2
Categorical

HIGH CARDINALITY
MISSING

Distinct725
Distinct (%)0.9%
Missing1349
Missing (%)1.7%
Memory size636.2 KiB
276
 
5296
428
 
5234
250
 
4778
427
 
3949
401
 
2906
Other values (720)
57900 

Length

Max length6
Median length3
Mean length3.166931042
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126 ?
Unique (%)0.2%

Sample

1st row250.6
2nd row507
3rd row490
4th row585
5th row532

Common Values

ValueCountFrequency (%)
2765296
 
6.5%
4285234
 
6.4%
2504778
 
5.9%
4273949
 
4.9%
4012906
 
3.6%
4962626
 
3.2%
5992604
 
3.2%
4032252
 
2.8%
4142076
 
2.5%
4112019
 
2.5%
Other values (715)46323
56.9%

Length

2022-02-01T17:23:34.592917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2765296
 
6.6%
4285234
 
6.5%
2504778
 
6.0%
4273949
 
4.9%
4012906
 
3.6%
4962626
 
3.3%
5992604
 
3.3%
4032252
 
2.8%
4142076
 
2.6%
4112019
 
2.5%
Other values (715)46323
57.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diag_3
Categorical

HIGH CARDINALITY

Distinct761
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size636.2 KiB
250
9166 
401
6606 
276
 
4112
428
 
3734
427
 
3156
Other values (756)
54638 

Length

Max length6
Median length3
Mean length3.111678868
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique114 ?
Unique (%)0.1%

Sample

1st row357
2nd row150
3rd row250
4th row250
5th row562

Common Values

ValueCountFrequency (%)
2509166
 
11.3%
4016606
 
8.1%
2764112
 
5.1%
4283734
 
4.6%
4273156
 
3.9%
4142938
 
3.6%
4962090
 
2.6%
4031886
 
2.3%
5851621
 
2.0%
2721605
 
2.0%
Other values (751)44498
54.7%

Length

2022-02-01T17:23:34.776932image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2509166
 
11.3%
4016606
 
8.1%
2764112
 
5.1%
4283734
 
4.6%
4273156
 
3.9%
4142938
 
3.6%
4962090
 
2.6%
4031886
 
2.3%
5851621
 
2.0%
2721605
 
2.0%
Other values (751)44498
54.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

number_diagnoses
Real number (ℝ≥0)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.421964821
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.2 KiB
2022-02-01T17:23:34.940709image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.931480463
Coefficient of variation (CV)0.2602384287
Kurtosis-0.07655270384
Mean7.421964821
Median Absolute Deviation (MAD)1
Skewness-0.8713996895
Sum604237
Variance3.730616778
MonotonicityNot monotonic
2022-02-01T17:23:35.121380image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
939501
48.5%
59144
 
11.2%
88506
 
10.4%
78379
 
10.3%
68133
 
10.0%
44428
 
5.4%
32238
 
2.7%
2824
 
1.0%
1167
 
0.2%
1638
 
< 0.1%
Other values (6)54
 
0.1%
ValueCountFrequency (%)
1167
 
0.2%
2824
 
1.0%
32238
 
2.7%
44428
 
5.4%
59144
 
11.2%
68133
 
10.0%
78379
 
10.3%
88506
 
10.4%
939501
48.5%
1014
 
< 0.1%
ValueCountFrequency (%)
1638
 
< 0.1%
158
 
< 0.1%
145
 
< 0.1%
1314
 
< 0.1%
125
 
< 0.1%
118
 
< 0.1%
1014
 
< 0.1%
939501
48.5%
88506
 
10.4%
78379
 
10.3%

blood_type
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size636.2 KiB
O+
32053 
A+
24744 
B+
9218 
O-
5689 
A-
4826 
Other values (3)
4882 

Length

Max length3
Median length2
Mean length2.041738319
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA+
2nd rowB+
3rd rowO+
4th rowAB-
5th rowA+

Common Values

ValueCountFrequency (%)
O+32053
39.4%
A+24744
30.4%
B+9218
 
11.3%
O-5689
 
7.0%
A-4826
 
5.9%
AB+2619
 
3.2%
B-1484
 
1.8%
AB-779
 
1.0%

Length

2022-02-01T17:23:35.368813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T17:23:35.516608image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
o37742
46.4%
a29570
36.3%
b10702
 
13.1%
ab3398
 
4.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hemoglobin_level
Real number (ℝ≥0)

HIGH CORRELATION

Distinct77
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.19232791
Minimum10.5
Maximum18.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.2 KiB
2022-02-01T17:23:35.655131image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10.5
5-th percentile12.6
Q113.4
median14.1
Q315
95-th percentile16
Maximum18.6
Range8.1
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.059999933
Coefficient of variation (CV)0.07468823574
Kurtosis-0.4492654524
Mean14.19232791
Median Absolute Deviation (MAD)0.8
Skewness0.1878605037
Sum1155425.8
Variance1.123599858
MonotonicityNot monotonic
2022-02-01T17:23:35.869607image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.62964
 
3.6%
13.92911
 
3.6%
13.72876
 
3.5%
13.82841
 
3.5%
13.52796
 
3.4%
14.12745
 
3.4%
142730
 
3.4%
14.22657
 
3.3%
13.42654
 
3.3%
13.32643
 
3.2%
Other values (67)53595
65.8%
ValueCountFrequency (%)
10.52
 
< 0.1%
10.82
 
< 0.1%
10.93
 
< 0.1%
115
 
< 0.1%
11.13
 
< 0.1%
11.218
 
< 0.1%
11.317
 
< 0.1%
11.433
< 0.1%
11.547
0.1%
11.671
0.1%
ValueCountFrequency (%)
18.61
 
< 0.1%
18.22
 
< 0.1%
18.12
 
< 0.1%
181
 
< 0.1%
17.92
 
< 0.1%
17.82
 
< 0.1%
17.72
 
< 0.1%
17.67
 
< 0.1%
17.516
< 0.1%
17.432
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.6 KiB
False
71697 
True
9715 
ValueCountFrequency (%)
False71697
88.1%
True9715
 
11.9%
2022-02-01T17:23:36.033031image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

max_glu_serum
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size636.2 KiB
None
54061 
NONE
23098 
Norm
 
1207
>200
 
1179
>300
 
1025

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNONE
4th rowNONE
5th rowNone

Common Values

ValueCountFrequency (%)
None54061
66.4%
NONE23098
28.4%
Norm1207
 
1.5%
>2001179
 
1.4%
>3001025
 
1.3%
NORM842
 
1.0%

Length

2022-02-01T17:23:36.117350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T17:23:36.253122image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
none77159
94.8%
norm2049
 
2.5%
2001179
 
1.4%
3001025
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

A1Cresult
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size636.2 KiB
None
67807 
>8
 
6547
Norm
 
4003
>7
 
3055

Length

Max length4
Median length4
Mean length3.764113399
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd row>7
3rd rowNone
4th row>8
5th rowNone

Common Values

ValueCountFrequency (%)
None67807
83.3%
>86547
 
8.0%
Norm4003
 
4.9%
>73055
 
3.8%

Length

2022-02-01T17:23:36.371990image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T17:23:36.515021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
none67807
83.3%
86547
 
8.0%
norm4003
 
4.9%
73055
 
3.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diuretics
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.6 KiB
False
79893 
True
 
1519
ValueCountFrequency (%)
False79893
98.1%
True1519
 
1.9%
2022-02-01T17:23:36.571071image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

insulin
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.6 KiB
True
44360 
False
37052 
ValueCountFrequency (%)
True44360
54.5%
False37052
45.5%
2022-02-01T17:23:36.615769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

change
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size636.2 KiB
No
43772 
Ch
37640 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowCh
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No43772
53.8%
Ch37640
46.2%

Length

2022-02-01T17:23:36.700155image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T17:23:36.828923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
no43772
53.8%
ch37640
46.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diabetesMed
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.6 KiB
True
62718 
False
18694 
ValueCountFrequency (%)
True62718
77.0%
False18694
 
23.0%
2022-02-01T17:23:36.872469image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

readmitted
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.6 KiB
False
72340 
True
9072 
ValueCountFrequency (%)
False72340
88.9%
True9072
 
11.1%
2022-02-01T17:23:36.916785image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Interactions

2022-02-01T17:23:21.356763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:48.383808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:50.767957image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:53.919323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:56.586781image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:59.240249image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:02.000669image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:04.504842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:06.942436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:09.184342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:11.808198image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:13.996242image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:16.238676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:18.712973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:21.563991image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:48.572833image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:50.955872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:54.148212image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:56.790977image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:59.410453image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:02.189944image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:04.702731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:07.120207image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:09.357841image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:11.974438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:14.162377image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:16.404191image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:18.891662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:21.766097image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:48.745076image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:51.170564image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:54.338367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:56.958092image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:59.588674image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:02.359651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:04.928588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:07.283863image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:09.540047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:12.139258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:14.333463image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:16.566309image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:19.054963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:21.972204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:48.920598image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:51.342976image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:54.518188image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:57.149877image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:59.744491image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:02.528487image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:05.104461image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:07.447823image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:09.712866image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:12.296784image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:14.495343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:16.761558image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:19.230876image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:22.163834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:49.084997image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:51.627419image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:54.745301image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:57.440946image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:59.908323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:02.683550image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:05.265396image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:07.599679image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:09.870116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:12.444847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:14.650049image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:16.941981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:19.400447image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:22.361490image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:49.247082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:51.785170image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:54.957613image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:57.604129image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:00.094129image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:02.834466image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:05.422858image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:07.752189image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:10.034441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:12.587588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:14.811587image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:17.101725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:19.570560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:22.539670image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:49.413241image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:51.969804image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:55.147496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:57.766832image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:00.285747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:02.994134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:05.576774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:07.913849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:10.205996image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:12.742309image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:14.967885image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:17.283590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:19.755950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:22.703352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:49.578064image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:52.167259image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:55.307131image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:57.996490image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:00.572257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:03.145746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:05.740462image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:08.068482image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:10.376090image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:12.896401image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:15.131788image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:17.488196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:19.916310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:22.866209image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:49.744485image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:52.355655image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:55.515823image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:58.196216image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:00.738367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:03.293891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:05.903023image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:08.228713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:10.545393image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:13.061339image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:15.285295image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:17.667405image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:20.358370image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:23.044904image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:49.918310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:52.549323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:55.689142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:58.374077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:00.937163image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:03.453429image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:06.080446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:08.391162image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:10.750373image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:13.220138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:15.449518image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:17.850220image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:20.538218image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:23.204586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:50.081650image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:52.730001image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:55.853000image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:58.531548image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:01.106982image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:03.592753image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:06.265820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:08.539837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:10.909714image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:13.362884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:15.606571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:18.001626image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:20.694126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:23.372006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:50.247756image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:52.914227image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:56.042849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:58.707526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:01.294195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:03.919367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:06.459024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:08.699604image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:11.298720image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:13.515258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:15.764451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:18.173802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:20.855816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:23.544949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:50.409805image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:53.212508image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:56.208902image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:58.868907image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:01.493460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:04.103091image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:06.610728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:08.844625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:11.457226image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:13.675561image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:15.914171image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:18.321736image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:21.009692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:23.730084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:50.583918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:53.569327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:56.406369image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:22:59.055502image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:01.793449image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:04.300165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:06.775121image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:09.019492image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:11.635006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:13.831300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:16.073266image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:18.521283image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-02-01T17:23:21.177510image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-02-01T17:23:37.025860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-01T17:23:37.361656image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-01T17:23:37.668272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-01T17:23:37.946594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-01T17:23:38.234528image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-01T17:23:24.170374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-01T17:23:25.722427image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-01T17:23:26.682348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-01T17:23:27.034843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

admission_idpatient_idracegenderageweightadmission_type_codedischarge_disposition_codeadmission_source_codetime_in_hospitalpayer_codemedical_specialtyhas_prosthesiscomplete_vaccination_statusnum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesblood_typehemoglobin_levelblood_transfusionmax_glu_serumA1CresultdiureticsinsulinchangediabetesMedreadmitted
00199042938CaucasianMale[50-60)?3.01.011??FalseComplete24.039.0000721250.63575A+14.5FalseNoneNoneNoNoNoYesNo
1191962954CaucasianMale[80-90)?2.01.073MCEmergency/TraumaFalseComplete50.008.00012765071509B+15.7FalseNone>7NoNoNoNoYes
22109707084EUROFemale[60-70)?1.01.075MD?FalseComplete43.0628.00004144902506O+13.0FalseNONENoneNoYesChYesNo
33157495374BlackFemale[70-80)?6.01.0172??FalseComplete58.058.00115775852509AB-13.5FalseNONE>8NoNoNoYesNo
4482692360CaucasianFemaleNaN?1.022.0712MC?FalseComplete56.0116.00025315325628A+13.0FalseNoneNoneNoNoNoNoNo
55218016576CaucasianFemale[70-80)?2.01.014HM?FalseIncomplete14.0313.00005782805629A+13.1FalseNoneNoneNoNoNoYesYes
66143084970CaucasianMale[60-70)?1.01.076BC?FalseComplete62.0021.00004824282769A-14.2FalseNone>7NoYesNoYesNo
77227644092OtherFemale[70-80)?3.01.0111?InternalMedicineFalseIncomplete18.049.0101V574385996O+12.9FalseNONENoneNoNoNoNoNo
8877740434CaucasianFemale[70-80)?1.03.072MC?FalseComplete36.009.0003250.86824966O-13.9FalseNONENoneNoYesNoYesNo
99203123016WHITEFemale[40-50)?3.01.012??FalseComplete5.0225.00004333624019A+13.2FalseNONENoneNoYesChYesNo

Last rows

admission_idpatient_idracegenderageweightadmission_type_codedischarge_disposition_codeadmission_source_codetime_in_hospitalpayer_codemedical_specialtyhas_prosthesiscomplete_vaccination_statusnum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesblood_typehemoglobin_levelblood_transfusionmax_glu_serumA1CresultdiureticsinsulinchangediabetesMedreadmitted
8140281402155324556OtherMale[50-60)?1.01.073MD?FalseIncomplete1.0013.0000682250.827079O+16.7FalseNoneNoneNoYesNoYesNo
8140381403224478630AfricanAmericanFemale[80-90)?3.06.027??FalseComplete35.0237.00204407074014A+13.8FalseNoneNoneNoYesChYesYes
8140481404228104460AfricanAmericanFemale[50-60)?3.01.013?ObstetricsandGynecologyFalseComplete8.0212.00002182186269AB-12.8FalseNoneNoneNoNoNoYesNo
814058140548436146BlackFemale[40-50)?1.01.076MCNephrologyFalseIncomplete35.0322.0000595591V459B+11.4FalseNoneNoneNoYesNoYesNo
814068140691915884CaucasianFemale[80-90)?1.03.074MD?FalseComplete46.0011.00005842762729A+13.8FalseNONENoneNoYesNoYesNo
814078140780746578CaucasianMale[60-70)?1.024.072MD?FalseComplete64.0022.0003486496250.925O+15.4FalseNoneNoneNoYesChYesNo
8140881408221853996AFRICANAMERICANFemale[70-80)?1.06.078CPInternalMedicineFalseComplete62.0219.00014404012769O+12.8FalseNoneNoneNoYesChYesNo
8140981409104846580BlackFemale[60-70)?1.022.075??FalseComplete1.0214.0000822403250.69B+13.0FalseNoneNoneNoNoNoNoYes
8141081410229820346CaucasianFemale[50-60)?3.01.021??FalseComplete30.0110.00009967532507AB+13.3TrueNoneNoneNoNoNoYesNo
814118141149302180African AmericanFemale[40-50)?1.01.073?InternalMedicineFalseComplete43.0014.0000250.12198V104B+12.4FalseNoneNoneNoYesChYesNo